Introduction

Having the goal of preparing athletes for long-term competition during the pre-season involves a complex approach that encompasses various parameters1. Strength and conditioning coaches must consider various factors beyond technical and tactical training and team building, such as the cardiovascular fitness of opponents, altitude and climatic conditions, rest, recovery strategies, and the mental state of the athletes2. In soccer, the coaches also need to be mindful of the suitable workload for players during training sessions3, and their adaptability to new phases4 by the beginning of the season, when matches are scheduled once or twice a week; however, circumstances such as half-season holidays, the new year, adverse environmental conditions such as freezing weather, or natural disasters like earthquakes can lead to long-duration pauses (over 2 weeks) in league activities. During these breaks or playing period changes, coaches must monitor players’ accumulated workload, physiological alterations, and body composition factors, as well as sleep and health-related qualities5,6. Restrictions on soccer players’ access to group training due to quarantine measures imposed during the COVID-19 pandemic have led to declines in performance indicators critical for soccer, such as sprinting and jumping7,8,9.

Strength and conditioning coaches can minimize the risk of injury and improve performance by optimizing return-to-play protocols during these intervals and understanding how these intervals affect performance. Generally, the running performance of players in a match can be directly influenced by various factors such as playing positions, age, fatigue due to running speed, duration of time, and many other factors. Conditions such as running at different speeds or maintaining minimal movement (e.g. standing or walking) at every moment of the match also significantly shape this performance10.

Returning to soccer after breaks due to the end-of-season, half-time, injury, or other factors can have both positive and negative effects on performance11. Concerning positive effects, rest and recovery, increased motivation, and physical regeneration may occur12. Adverse effects can be expressed as deconditioning, technical fatigue, and risk of injury. Nakisa and Rahbardar13 found that depriving soccer players of training for 5 weeks decreased endurance by 24%. Various variations can be observed in soccer players’ performance after various soccer breaks. One of the most important of these is sprint performance. Analyzing players’ sprint performance involves assessing how they maintain or improve their sprinting ability after breaks14. Savićević et al. compared the running performance of players in matches played before and after COVID-19 infection. They found a significantly lower number of high-intensity accelerations in the running performance of soccer players in matches played after COVID-19 infection15. Further, the total distance traveled, and the distance traveled at very high speed decreased after quarantine compared to the pre-quarantine period for soccer teams in the Italian Serie A16. Moreover, the COVID-19-induced quarantine harmed the teams’ performances when comparing the pre- and post-quarantine periods, leading to an overall decrease in the mean values of the measured spatial and physical variables17.

Sprint performance in soccer is a critical aspect of the game and affects both offensive and defensive situations. Soccer players often perform short, high-intensity sprints to beat opponents, create goal-scoring opportunities, or recover defensively. High-speed running actions or sprints are considered a prerequisite for successful performance in soccer18,19,20. Improving sprint performance is regarded as a vital component of athletic performance within the framework of soccer rules. High-intensity movements during a soccer match can be classified as actions requiring acceleration (10 m sprint), actions at maximum speed (30 m sprint), or actions requiring agility21. Maximum speed and acceleration are important in field sports, and running speed over short distances is the basis for success22. Stølen et al.23 reported that 96% of sprint events during a soccer match were shorter than 30 m, while 49% were shorter than 10 m.

There are some research that analyzed the performance after a pause in unusual situations such as COVID-19 lockdowns24,25, however, to the authors’ knowledge, no studies have analyzed performance after a pause in natural disasters such as earthquakes in Turkiye or even DANA flood in Valencia. Therefore, this study aimed to examine (i) the impact of prolonged pauses (e.g., World Cup (WC) stoppage and earthquake (EQ) stoppage) on running displacements and ball-in-play duration and (ii) the relation between performance metrics and outcomes. We hypothesized that unplanned prolonged pauses (e.g., the WC and EQ stoppages) in the season would significantly decrease team performance, including running displacements and ball-in-play duration. Moreover, these reductions in performance metrics would be negatively correlated with match outcomes. We anticipate that teams may exhibit variations in running patterns and match outcomes during these periods, influenced by factors such as player readiness, adaptation to official matches, international commitments, and variations in physical conditioning. We predict these differences will be reflected in metrics such as total distance covered, running speeds, and accelerations, with potential implications for match success or failure.

Materials and methods

Subjects

The current research analyzed data from 19 Spor Toto Turkish Super League teams (i.e., 550 players). There was a 5-week break after the 14th week due to the FIFA WC Qatar 2022 (Fig. 1). As of week 23, there was an earthquake in Türkiye, and two clubs withdrew from the league after the 14th week. The teams that withdrew from the league were not dropped without prejudice to their rights. Dropped teams were deemed 3-0 forfeit losers in matches against their opponents as of the withdrawal date. Therefore, the Turkish Super League was completed with 17 teams. The necessary permission to use the data was obtained from the Turkish Football Federation. The data supporting this study’s findings are openly available in Figshare at https://doi.org/10.6084/m9.figshare.25211174. The study was conducted according to the guidelines of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Aksaray University (2024/03-175 / E-34183927-020-00000960830).

Fig. 1
figure 1

Weekly match duration in the Turkish Super League 2022–2023 season. Twice the active season paused for a long time due to the WC (for 5 weeks) and EQ tragedy (for 3 weeks).

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Procedures

This research focuses on the Turkish Super League during the 2022/23 season. In this season, two extended pauses occurred due to the Qatar WC 2022, starting from November 20, 2022, for four weeks (in the middle of the season), and a three-week pause due to the occurrence of the Türkiye EQ on February 6, 2023. During the first long pause before the WC, clubs independently postponed their training sessions for several days. Meanwhile, 19 players from 8 clubs joined their national teams and resumed club training after their national teams were eliminated. According to reports, many players engaged in humanitarian aid for several days without previous planning during the second long pause after the EQ. Based on its policies, each club postponed team training sessions and awaited the official new schedule from the federation. The number of total observations according to each period before WC was n = 248, between WC and EQ stoppages was n = 165, and after the EQ was n = 213. All technical performance data of the research were obtained using the InStat Sport (Instat, Moscow, Russia) and categorized based on various parameters, including soccer field coverage at different speeds, high accelerations, duration of the ball in play, periods, and match results. Numerous soccer teams have employed this method, which tracks the players’ performance indicators during the game since it gives the coaches the data they need quickly and accurately26.

The total covered distance and displacement were analyzed across speed categories: less than 0.2 m/s, 0.2–2 m/s, 2.1–4 m/s, 4.01–5.5 m/s, 5.51–7 m/s, and over 7 m/s. High-speed action data were divided into groups: 5.5–7 m/s (high-speed) and maximum speed (over 7 m/s)27,28. The match ball-in-play duration was divided into attacking and defending phases and build-up.

Considering the two significant pauses occurred throughout the Turkish League (WC and EQ), the number of matches the data were available in this study until WC was 248. After WC and before, the EQ (Between Stoppages) was 165, and after the EQ was 213 matches. In these matches, the number of winning teams was 219, the losing teams were 261, and the draw matches were 146 (data of some teams are unavailable). This study focused on three distinct periods, where players commenced matches after a period of strength and conditioning, requiring adjustments in their daily activities and rest to align with the match season.

Statistical analysis

Data are expressed as mean, standard deviation, minimum, and maximum. A one-way ANOVA test was used to determine whether there were any statistically significant differences between the means of independent (unrelated) groups. Independent parameters such as field coverage at different speeds and accelerations, and total ball-in-play duration containing attacking, defending, and build-up times are segmented and compared in other periods or match outcomes. To account for the non-independence of data sampled from the same individuals across multiple matches, separate linear mixed models were performed. Furthermore, the magnitude of the correlation between and within groups was analyzed using the Tukey–Kramer HSD post-hoc test to provide adjusted p-values for possible pairwise comparisons and applying the D’Agostino-Pearson test for normal distribution. The resulting correlation coefficients (r) were classified as trivial (r < 0.1), small (r = 0.1–0.3), moderate (r = 0.3–0.5), large (r = 0.5–0.7), very large (r = 0.7–0.9), and almost perfect (r > 0.9)29,30. A significance level of p < 0.05 was adopted a priori, and the calculated p-value indicates a meaningful difference among parameters in different periods when the value is equal to or less than 0.05. The data were analyzed using Orange 3 an open-source machine learning visualization software tool for Windows, version 3.36.231 and MedCalc Statistical Software version 19.2.6 (MedCalc Software Ltd, Ostend, Belgium).

Results

The processed data were analyzed and compared across three match periods (before WC, between stoppage, after EQ), focusing on key metrics such as total covered distance, running speeds, ball-in-play duration, and their correlation with match outcomes. Table 1 displays the mean and p values and variances for each parameter compared in different periods. Additionally, Fig. 2 illustrates the box plot graph depicting the total distance covered in the same periods. Significant differences (p < 0.001) were found for the periods Before WC, Between Stoppages, and after EQ. When comparing the mean of running speeds, number of speed actions, and match duration with the Tukey-Kramer test in different periods (Before WC, After WC until EQ occurrence (Between Stoppages), and after EQ. Significant correlations (p < 0.05) were found for total distance, distance covered over 7 m/s, ball in play duration, and number of maximum speed actions over 7 m/s in all the periods. Additionally, significant correlations (p < 0.05) were found in build-up durations covered distance and number of high-speed actions between 5 and 7 m/s in Between Stoppages period with Before WC and After EQ, and covered distance under 0.2 m/s Between Stoppage and Before WC periods (Fig. 2).

Table 1 Comparing the mean of running speeds, number of speed actions, and match duration (± SD) with the Tukey–Kramer test in different periods (before WC, after WC until EQ occurrence (between stoppages), and after EQ.
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Fig. 2
figure 2

Comparing the total distance covering the field (km) in different periods in a Box Plot graph.

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The Box Plots in Fig. 2 show the comparison of the total distance covered on the field, Fig. 3 shows the comparison build-up duration, and Fig. 4 shows the comparison of the number of running efforts with high speeds (over 7 m/s) in different periods and the mean value of their covered distance. On average, players ran more, circulated the ball to build-up more, and accelerated more after the World Cup tournament until the earthquake tragedy. When comparing the build-up duration (minutes) in different periods in a Box Plot graph, significant differences (p = 0.02) were found for the periods Before WC, Between Stoppages, and after EQ (Fig. 3).

Fig. 3
figure 3

Comparing the build-up duration (minutes) in different periods in a Box Plot graph.

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Fig. 4
figure 4

Comparing the number of high speed actions over 7 m/s in different periods in a Box Plot graph.

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When comparing the number of high-speed actions over 7 m/s in different periods in a Box Plot graph. Significant differences (p < 0.001) were found for the periods Before WC, Between Stoppages, and after EQ (Fig. 4).

Moreover, the match results are compared with similar parameters, revealing a significant difference among displacements with very low and very high velocities. Table 2 provides each parameter’s calculated mean and variance numbers in the win, lose, or draw cases. Significant differences (p < 0.05) were observed for total distance, running speed over 7 m/s, and the number of maximum speed actions over 7 m/s with match outcomes (Table 2).

Table 2 Mean of different ranges of speeds, number of speed actions, and attacking and defending duration (± SD) with match outcomes.
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A level 3 Tree graph in Fig. 5 illustrates the total pitch covering results. The teams that covered less than 105 km during matches were significantly more likely to lose, highlighting the high level of players’ readiness in the Turkish Super League.

Fig. 5
figure 5

Results occurring across different team running displacements (in km) by the small pie chart near each covered distance range. The pie charts in this tree graph represent the outcomes, green denotes wins, red represents losses, and blue indicates draws. The thickness of the connection lines is relative to the parent. The edge width corresponds to the proportion of instances in the nodes concerning the instances in their parent node. This graph highlights that player readiness to cover more ground is a key factor in coaches’ training programs where the teams that run less than the standard of 105 km significantly diminish their victory chances.

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Finally, Table 3 presents the correlation between various speed ranges, acceleration levels, and playing times with total covered distance and ball-in-play duration. The results are compared with match results, highlighting significant correlations for specific speed ranges, acceleration values, and ball-in-play durations. Comparing total covered distance, significant correlations (p < 0.05) were found for speeds over 2.01–7 m/s, number of high-speed actions over 5.5 m/s, and ball-in-play duration. Comparing ball in-play duration, significant correlations (p < 0.05) were found for speeds between 2 and 5.5 m/s, number of high-speed between 5.5 and 7 m/s, total covered distance, and build-up duration (Table 3).

Table 3 Correlation between a different range of speeds, number of speed actions, and playing times with total covered distance and ball in-play duration.
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Discussion

The results of this study highlight that different types of breaks during a season have varying impacts on player performance. During the 2022 FIFA WC break, many players who were not selected for their national teams continued regular training sessions. As a result, their performance in subsequent matches did not show significant declines. However, during the EQ, training was interrupted, and players could not maintain their physical conditioning due to unforeseen circumstances. This likely contributed to the notable decline in performance metrics observed post-earthquake. These findings suggest that training during planned breaks, such as for international tournaments, can be optimized to maintain performance levels. Conversely, alternative training strategies must be employed to mitigate performance loss during unplanned interruptions, such as natural disasters. Tailored return-to-play protocols should be considered to account for the specific circumstances of the break. In a systematic review study of physical match performances before and after Covid-19 in professional European Football leagues; 1 study from the Polish league, 1 study from the Croatian league, 2 of 3 studies from the Spanish league and 1 study from the Italian league showed that total distance and high-intensity running distances decreased compared to before the pandemic9.

Performance analysis in soccer is an ever-evolving field that has gained significant attention in recent years32. Understanding play styles is crucial for practical performance assessment, not only for coaches but also for all stakeholders involved in soccer33. Identifying performance indicators associated with success is essential, yet predictive analyses in soccer have been underutilized, leaving gaps in our understanding of the determinants of success. This study addresses this gap by comparing performance metrics in the Turkish Super League across different periods, mainly focusing on the effects of extended pauses such as those caused by the FIFA WC and an EQ.

By examining running patterns, match outcomes, and other factors, we seek to provide insights into the factors influencing success or failure in soccer matches based on performance analyses. The main result of the analyses showed that leading up to the WC competition, the average total displacement and running were lower than in the period after the WC. Several hypotheses could explain this, such as players adapting to official matches after the summer transfer market, some teams participating in European competition play-offs and group stages, the concentration of some international players on their national teams for the WC, and variations in physical readiness and conditioning. LaLiga players who participated in the FIFA World Cup Qatar 2022 showed a significant decrease in maximum speed performance after the tournament34.

The significant values in Table 1 (p < 0.001 or p = 0.002) illustrate that total displacement, lower speed under 0.2 m/s and higher speed over 7 m/s, and the number of accelerations over 5 m/s (p < 0.001) differed after WC to the EQ time with other periods. Thus, the significant relationship between running speed and results indicated that teams exhibiting more static behavior during matches (covering more distance at speeds less than 0.2 m/s) had a higher probability of losing. Conversely, teams running at speeds exceeding 7 m/s were likelier to win. Also, the build-up duration (p = 0.002) and ball-in-play time (p < 0.001) notably had more duration in that period. Therefore, enhancing speed and stamina are essential components of soccer training since they directly affect players’ performances and the chance that their team will win. Particular activities and exercises are usually included in training sessions to improve these qualities35.

We identified that winners were more likely to engage in a more significant number of accelerations and run faster than 7 m/s. In addition, the probability of losing by covering the field with a speed less than 0.2 m/s (being static) has a significant result. Furthermore, during this period, there was an extension in the duration of the ball in play. Lastly, more distance covered during a match is related to a winning outcome. Distance covered at high intensity is critical for the outcome of the game36,37. In addition, when sprint distance is taken into account, winning teams performed more sprint distance and sprint actions than losing teams38. Modric et al. found that in the 2020–2021 UEFA Champions League, winning teams covered more distance in high-speed running (5.5–7 m/s) and sprinting (> 7 m/s) than losing teams39. On the other side, Fig. 5 indicates a significantly low probability of obtaining any points when a team in the Turkish league covers less than 105 km of pitch distance; in 87.5% of the matches, the team that covered less distance lost. Therefore, physical readiness to run more to cover more distance should be a severe topic to Turkish Super League coaches. Furthermore, players with longer stamina may run hard for the full ninety minutes without fatigue, improving their teams’ chances of winning due to their ability to play at a high level until the last whistle.

In terms of players’ readiness shown in Table 3, the teams covering the pitch in each match at speeds ranging from 2.01 m/s to 4 m/s and 4.1 m/s to 5.5 m/s exhibit a very large correlation with covering more distances (r = 0.869 & r = 0.835). A significant correlation exists between running speeds from 5.51 m/s to 7 m/s and total covered distance (r = 0.659). Some speed ranges also show a moderate correlation with increased ball-in-play duration in 4.01–5.5 m/s (r = + 361) and 2.01 m/s to 4 m/s speed range (r = 0.488). In general, there is a moderate correlation between covering more distance and the duration of ball-in-play (r = 0.497). Based on the results of the current study, soccer players are more likely to win games if they can sprint faster and further during Turkish professional soccer matches since soccer players need speed and endurance as essential physical traits to execute tactical strategies and maintain high-intensity efforts throughout the game. Quicker players can switch from defense to attack more rapidly, open scoring opportunities, and pressure opponents. Research has found that a team with stronger sprinting ability on offense is more likely to win the game40. In another study, when teams won the game, the number of sprints was higher and players covered more distance during the offensive play41.

Ultimately, in a few matches, data from a team was missing, although more complete data could provide the most precise results. During the Turkish league season, some matches were canceled because two teams withdrew, leading to unplanned pauses, or were not officially played when a team was guaranteed not to be relegated due to the EQ by the federation’s decision. These factors contributed to unplanned interruptions, affecting team performance after the EQ period. While breaking the effects of planned or unplanned pauses is difficult, minimizing pauses during a soccer season is critical via prescribing appropriate training to maintain fitness.

In conclusion, this study highlights how planned and unplanned prolonged interruptions, significantly affected team performance metrics, particularly running patterns and accelerations. While planned breaks like the World Cup allowed teams to maintain performance through structured training, unplanned breaks such as the earthquake resulted in a noticeable decline in physical readiness and match intensity. These findings underscore the importance of tailored training programs to mitigate the negative effects of unexpected interruptions and maintain competitive performance during a disrupted season. The players readiness effects on the ability to sprint faster and cover more distance is vital for soccer success, enabling players to execute tactics and maintain intensity. Thus, it requires to design training strategies during breaks to preserve these key attributes and sustain competitive performance.